Multiphase Ozonolysis of Oleic Acid-Based Lipids: Quantitation of Major Products and Kinetic Multilayer Modeling
Bibliographic record
Abstract
Commonly found in atmospheric aerosols, cooking oils, and human sebum, unsaturated lipids rapidly decay upon exposure to ozone, following the Criegee mechanism. Here, the gas-surface ozonolysis of three oleic acid-based compounds was studied in a reactor and indoors. Under dry conditions, quantitative product analyses by 1 H NMR indicate up to 79% molar yield of stable secondary ozonides (SOZs) in oxidized triolein and methyl oleate coatings. Elevated relative humidity (RH) significantly suppresses the SOZ yields, enhancing the formation of condensed-phase aldehydes and volatile C9 products. Along with kinetic parameters informed by molecular dynamics simulations, these results were used as constraints in a kinetic multilayer model (KM-GAP) simulating triolein ozonolysis. Covering a wide range of coating thicknesses and ozone levels, the model predicts a much faster decay near the gas–lipid interface compared to the bulk. Although the dependence of RH on SOZ yields is well predicted, the model overestimates the production of H 2 O 2 and aldehydes. With negligible dependence on RH, the product composition for oxidized oleic acid is substantially affected by a competitive reaction between Criegee intermediates (CIs) and carboxylic acids. The resulting α-acyloxyalkyl hydroperoxides (α-AAHPs) have much higher molar yields (29–38%) than SOZs (12–16%). Overall, the ozone–lipid chemistry could affect the indoor environment through “crust” accumulation on surfaces and volatile organic compound (VOC) emission. In the atmosphere, the peroxide formation and changes in particle hygroscopicity may have effects on climate. The related health impacts are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".